The new generation of extragalactic imaging surveys of galaxies, such as Euclid [1]} or LSST [2]}, will deliver an unprecedented volume of data. These new surveys also imply more demanding scientific requirements which are translated into new challenges from the data analysis perspective. The deeper the survey, the larger the density of detected sources, leading to an increase of the probability of observing overlapping galaxies due to projection effects. We call those objects blended, and the task of identifying and treating that confusion, deblending. Misidentifying blended galaxies significantly impacts the error budget and can even prevent reaching the scientific requirements for precision cosmology [3]}.
